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Found 4 papers, 0 papers with code
Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction
While systems that pass the output of traditional multi-view stereo approaches to a network for regularisation or refinement currently seem to get the best results, it may be preferable to treat deep neural networks as separate components whose results can be probabilistically fused into geometry-based systems.
Scalable Uncertainty for Computer Vision with Functional Variational Inference
As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world.
X-Section: Cross-Section Prediction for Enhanced RGB-D Fusion
Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years.
X-Section: Cross-Section Prediction for Enhanced RGBD Fusion
Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years.
